Tal Arbel

Google Scholar

Gian Favero

Maîtrise recherche - McGill University

gian.favero@mila.quebec

Site web

mohamed.mohamed@mila.quebec

Joshua Durso-finley

Doctorat - McGill University

dursofij@mila.quebec

Mohamed Mohamed

Maîtrise recherche - McGill University

Nima Fathi

Maîtrise recherche - McGill University

nima.fathi@mila.quebec

Site web

parham.saremi@mila.quebec

Parham Saremi

Maîtrise recherche - McGill University

zahra.tehraninasab@mila.quebec

Xing Shen

Maîtrise recherche - McGill University

xing.shen@mila.quebec

Maîtrise recherche - McGill University

Publications

Prediction of Progression in Multiple Sclerosis Patients

Adrian Tousignant

Paul Lemaitre

Doina Precup

Douglas Arnold

We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from… (voir plus) baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66 +- 0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701 +- 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.

2019-02-28

MIDL.io/2019/Conference (poster)

openreview.net

Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

Lena Maier-Hein

Matthias Eisenmann

Annika Reinke

Sinan Onogur

Marko Stankovic

Patrick Scholz

Hrvoje Bogunovic

Andrew P. Bradley

Aaron Carass

Carolin Feldmann

Alejandro F. Frangi

Peter M. Full

Bram van Ginneken

Allan Hanbury

Katrin Honauer

Michal Kozubek

Bennett Landman

Keno März

Oskar Maier … (voir 18 de plus)

Klaus Maier-Hein

Bjoern Menze

Henning Müller

Peter F. Neher

Wiro Niessen

Nasir Rajpoot

Gregory C. Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

Christian Stock

Danail Stoyanov

Abdel Aziz Taha

Fons van der Sommen

Ching-Wei Wang

Marc-André Weber

Guoyan Zheng

Pierre Jannin

Annette Kopp-Schneider

2019-01-30

Nature Communications (publié)

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas

Mauricio Reyes

Andras Jakab

Stefan. Bauer

Markus Rempfler

Alessandro Crimi

Russell T. Shinohara

Christoph Berger

Sung-min Ha

Martin Rozycki

Marcel W. Prastawa

Esther Alberts

Jana Lipková

John Freymann

Justin Kirby

Michel Bilello

Hassan M. Fathallah-Shaykh

Roland Wiest

J. Kirschke

Benedikt Wiestler … (voir 31 de plus)

Rivka R. Colen

Aikaterini Kotrotsou

Pamela LaMontagne

D. Marcus

Mikhail Milchenko

Arash Nazeri

Marc-André Weber

Abhishek Mahajan

Ujjwal Baid

Dongjin Kwon

Manu Agarwal

Mahbubul Alam

Alberto Albiol

A. Albiol

Alex A. Varghese

T. Tuan

Aaron J. Avery

Bobade Pranjal

Subhashis Banerjee

Thomas H. Batchelder

Nematollah Batmanghelich

Enzo Battistella

Martin Bendszus

E. Benson

José Bernal

George Biros

Mariano Cabezas

Siddhartha Chandra

Yi-Ju Chang

et al.

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneo… (voir plus)us histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

2018-11-05

ArXiv (prépublication)

Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation

Tanya Nair

Doina Precup

Douglas Arnold

2018-09-26

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (publié)

CNN Prediction of Future Disease Activity for Multiple Sclerosis Patients from Baseline MRI and Lesion Labels

Nazanin Mohammadi Sepahvand

Tal Hassner

Douglas Arnold

2018-09-16

BrainLes@MICCAI (publié)

3D U-Net for Brain Tumour Segmentation

Raghav Mehta

2018-09-16

BrainLes@MICCAI (publié)

How to Exploit Weaknesses in Biomedical Challenge Design and Organization

Annika Reinke

Matthias Eisenmann

Sinan Onogur

Marko Stankovic

Patrick Scholz

Peter M. Full

Hrvoje Bogunovic

Bennett Landman

Oskar Maier

Bjoern Menze

Gregory C. Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

F. V. D. Sommen

Guoyan Zheng

Henning Müller

Michal Kozubek

Andrew P. Bradley

Pierre Jannin … (voir 2 de plus)

Annette Kopp-Schneider

Lena Maier-Hein

2018-09-13

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (publié)

RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours

Raghav Mehta

2018-09-12

Simulation and Synthesis in Medical Imaging (publié)

Structured deep Fisher pruning for efficient facial trait classification

Qing Tian

James J. Clark

2018-09-01

Image and Vision Computing (publié)

Fisher Pruning of Deep Nets for Facial Trait Classification

Qing Tian

James J. Clark

Although deep nets have resulted in high accuracies for various visual tasks, their computational and space requirements are prohibitively h… (voir plus)igh for inclusion on devices without high-end GPUs. In this paper, we introduce a neuron/filter level pruning framework based on Fisher's LDA which leads to high accuracies for a wide array of facial trait classification tasks, while significantly reducing space/computational complexities. The approach is general and can be applied to convolutional, fully-connected, and module-based deep structures, in all cases leveraging the high decorrelation of neuron activations found in the pre-decision layer and cross-layer deconv dependency. Experimental results on binary and multi-category facial traits from the LFWA and Adience datasets illustrate the framework's comparable/better performance to state-of-the-art pruning approaches and compact structures (e.g. SqueezeNet, MobileNet). Ours successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG-16, 82% for GoogLeNet) and with significant FLOP reductions (83% for VGG-16, 64% for GoogLeNet).

2018-03-21

ArXiv (prépublication)

Task-specific Deep LDA pruning of neural networks

Qing Tian

James J. Clark

With deep learning's success, a limited number of popular deep nets have been widely adopted for various vision tasks. However, this usually… (voir plus) results in unnecessarily high complexities and possibly many features of low task utility. In this paper, we address this problem by introducing a task-dependent deep pruning framework based on Fisher's Linear Discriminant Analysis (LDA). The approach can be applied to convolutional, fully-connected, and module-based deep network structures, in all cases leveraging the high decorrelation of neuron motifs found in the pre-decision layer and cross-layer deconv dependency. Moreover, we examine our approach's potential in network architecture search for specific tasks and analyze the influence of our pruning on model robustness to noises and adversarial attacks. Experimental results on datasets of generic objects, as well as domain specific tasks (CIFAR100, Adience, and LFWA) illustrate our framework's superior performance over state-of-the-art pruning approaches and fixed compact nets (e.g. SqueezeNet, MobileNet). The proposed method successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG16, up to 82% for the already compact InceptionNet) and with significant FLOP reductions (83% for VGG16, up to 64% for InceptionNet). Through pruning, we can also derive smaller, but more accurate and more robust models suitable for the task.

2018-03-21

(publié)

www.semanticscholar.org

Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight cases

Ian J. Gerard

Marta Kersten-Oertel

Simon Drouin

Jeffery A. Hall

Kevin Petrecca

Dante De Nigris

Daniel A. Di Giovanni

D. Louis Collins

Abstract. We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented … (voir plus)reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient’s cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.

2018-01-26

Journal of Medical Imaging (publié)